Claude Code for Finance + The Global Memory Shortage: Doug O'Laughlin, SemiAnalysis | Latent Space: The AI Engineer Podcast

this crap makes mistakes all the time, all the time.

It is still just like a, like I think of it once again as like a junior analyst, right?

The analyst goes and does all this like really pain in the ass information and you bring it all together to make a good decision at the top.

Historically, what happens is that junior analyst who I once was went and gathered all that information.

And, after doing this enough times, there's a meta-level thinking that's happening where it's like okay.

Here is what I really understand and how this type of analysis I'm an expert in.

Actually, I'm very good at it.

I consistently have a hit rate.

Now I'm the expert, right?

I don't think that meta-level learning is there yet.

We'll see if L1s do it, right?

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

It better happen if you're spending a trillion dollars and there's not meta-level learning.

But for me in our firm, that massively amplifies everyone who is an expert.

Because you have to still do something.

You can't just slop it up.

It's very obvious to me what it's slopped.

Welcome to Lean Space.

Yeah, thank you for having me, man.

I, after all this time, I just, is it okay if I just call you Swix?

I feel like that's where my brain is.

I've known you for so long.

You can call me Mule if you want.

I'm not scared.

You know, yeah.

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

It's been a long time coming.

I think I first met you at like new orleans or like one of the one of the new yorkses.

Yeah yeah, i mentioned one of the new yorkses in purpose.

I think it was vancouver.

Yeah, I don't know.

It was like some after party.

Yeah, yeah, yeah.

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

I'm like, oh, OK.

Yeah.

Well, I mean, it's just like I knew about you and we've like been in that you know pen pals for a long time.

So like cool meeting in person.

Yeah.

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

So, yeah.

Amazing.

I didn't go to the New Orleans one.

I really wish I did.

I love New Orleans, obviously.

There are two New Orleans's in a row.

And yeah, honestly, we should go back there.

Yeah.

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

I don't even think that far out.

But that sounds pretty interesting to me.

I can't remember which one.

There's something in Korea this year, right?

Yeah, I think ICML.

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.

Well, yeah.

I mean, I'm glad to catch you.

I mean, I am traveling to you.

Yeah.

Thank you.

I really appreciate it.

Yeah.

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

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.

Yeah, yeah, it's a pleasure.

And so you want to go back.

You used to be anonymous.

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

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

That's the...

Yeah, do you know how I noticed you?

I was just like, oh, this guy seems smart.

Yeah, I don't know, dude.

I mean, I remember noticing you too.

So it's like, you know, this was in the early, like the primordial days of Twitter.

Honestly, I miss those the most.

It was like 2018.

17 18, something like that yeah, but yeah yeah, i remember from value 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 possibly have.

And then yeah, i have another account and i actually have a third account which is my main account these days.

Yeah,

Oh, which one is that?

Okay.

I don't want to dox your other account.

Oh, okay.

So, so, so it is there.

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

That's like my oldest finance account.

I think of it as my legacy account.

Okay.

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

I feel like.

Yeah.

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

Yeah.

Same profile pic, you know, so.

Yeah.

So let's, let's do a little bit of the Doug story.

Cause a lot of people hear about Dylan and I wanted to just make this the Doug story, make 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 semi is, is that the story?

No, actually, I solo nerd stymied myself.

So I wouldn't say value, because 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 stymied me, all the like single shot me, is I found ASML.

And I like fell in love with it.

And then I like after ASML, I just like read about all this stuff.

How complicated it's to make these.

Who are the people who are able to make them?

And then, you know, semiconductors, the whole downstream is all from there.

But it started with ASML in 2018.

I really fell in love with it.

And then I read like textbooks and I just like kept going deeper.

And my favorite part about doing that.

I'm going to pull out the Asianometry.

Yeah, that's perfect.

That's a perfect one.

John.

Amazing.

John's a monster, honestly.

This one, right?

Yeah.

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

Every single aspect of what goes into it.

And what's truly great about it It's all science fiction.

Like that's.

My favorite thing is like science fiction exists other than you know the talking perfectly intelligent robot, whatever information L1.

Yeah.

ASML is all science fiction.

So the semiconductor stuff has 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.

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

Yeah, I did know.

I covered TMT, which is a freaking huge sector to cover.

It's absurdly huge.

Yes, very large.

I was covering Sprint.

Yeah, yeah, yeah.

Like, you know, Viacom.

Yep.

And then there's ASML.

Yeah.

The N, I feel like the T and the M and the T are actually three completely separate industries.

But once upon a time, I think in 2000, they were kind of really close together.

Yeah.

Right.

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

Yeah.

Well, I mean, this is my reflection is like I used to be in.

I used to be, I guess, our tech sector guy.

And like I did the flights to Taiwan and I took those meetings with like, Credit Suisse and all those, 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 it's really hard as an investor to make the connection to okay wow, that was at me for this quarter.

Or at least this year.

Because one, there's just so much foundational knowledge.

And then you're like, well, okay, everything here is taken for granted.

It's already priced in.

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 believed morris 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 chat GPT knew everything, or You had to go and read these primers of all this information.

They're like, oh, it's a very mature industry.

It matured.

It used to be really immature in the 80s and 90s and 2000s.

But now you're consolidated.

Growth doesn't go up a lot.

And everyone kind of had this old playbook from the early 2000s.

A lot of people hated hardware.

There's just this perception that semiconductors weren't valuable. weren't as valuable.

Actually, software was the most valuable thing.

Now software is getting shit on, but that's like outside of the scope of this.

But people just thought it was this old, mature business that had nothing new under the sun.

Meanwhile, every single day, just making a new chip was like science fiction.

People took that for granted.

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

And what happened for Semiconductor specifically?

Is it created a lot of pricing, power or value for everyone who knew how to make a good chip?

So NVIDIA is probably the best case.

You could talk about parallel computing and all that stuff, but it's not just like.

They know every aspect of it, from the chip to the networking, to the design, to the scale up, the whole thing.

It is like, you know, versus in the past, it was just CPU gets better, go brr, right?

And so I think that I had a really deep belief that in this case, that Moore's law was, Moore's law was ending and everything would change.

And so coming in with that like thesis at the top level just like made me want to attack every little assumption and something that really changed as well.

I, and dude, this is honestly my, my favorite post I've ever written.

It's like 20, it's like a check GPT three and the writing on the wall.

In like 2020.

You know my early.

I get an early pitch to for fabricated knowledge and I'm like Hey, you know I'm going to make a release.

You know Moore's laws 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 good long range prediction, I feel like.

I just don't think I would have.

I 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 DMs and stuff like that.

Was it just online?

It was online.

We met in Taiwan.

No, no, no.

I've actually only been to Taiwan with him one time, I think.

So, I mean, like, look, we just met in person.

We yapped, we went to conferences.

But I think that that's like kind of we are both really early to the thesis, kind of have a different background and perspective.

Dylan is technology first.

And, you know, obviously, 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 thing that's crazy is like We called it, we were right.

Blah blah blah, blah blah.

But, like the thing that I think that still shocks me all the time is the magnitude of how right we are, you know.

Like it'd be like, oh, NVIDIA was good, right?

NVIDIA is pretty good.

And then it's like, no, NVIDIA is now the most valuable company in the world.

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

I still wouldn't have put that together, or like I wouldn't have believed it, if you.

This is one of many theses at the time, exactly yeah yeah, like there's so many things like what else are you writing at the time?

Right, that didn't work out, you know?

Yeah yeah, we can look, we can look back, but I I, I.

I'm pretty happy with my long-term track record.

I really am.

But yeah, I'm just really surprised the magnitude of how everything happened.

It's crazy to me that co-ops is not a household term, but relatively well-known.

It was like an exotic technology.

So all this stuff has been this like learning journey really believing where technology is going, why chips are so important, and then obviously understanding the big scheme of all the things putting it together.

And so that's the, yeah, that was like the early days.

And it's, I think it's all been downstream of that, like, you know, one goaded insight, pretty much.

Yeah.

I mean, and probably like a career maker right there, you know, and 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.

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 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 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.

Yeah.

Cool.

We're going to talk about other trends that you've spotted, primarily like the sort of memory cycle, but also optics, which is an 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.

Yeah, I am.

Where's the chart with the four? four percent of code it's 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 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 led it.

And it's weird because I think of you as like an analyst, right?

Like one of Semi-Analyst's alphas is that you're kind of like the fun millennial analyst semiconductor firm when everyone else is super boring and old.

But like, what are you doing, you know, getting so into cloud code?

Like you know, shouldn't you be reading reports and stuff you know?

Like tell the story of your code psychosis.

So yeah, I think here's the thing is if you want to be good at any game, we're tool users at the end of the day, right?

If you are good, if you want to be like and obviously this is like outside of my job as an analysis like I have all these other things I need to do to grow and make some 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 Chachapati Anthropic, all this kind of stuff.

And I've been pretty, like, I'm an early adopter in tools as much as I can be.

And like, for example, I've been running our case study that we have into Cloud Code since it first came out.

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

So which case study?

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

Hey, you know, can you, can you take this company and do some analysis blah blah, blah.

Give us this format back.

And I've been running it through like the agentic things that are like hey, 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 joke like, well you know, they're going to start to beat the worst submissions.

And so like that was that was always my base level.

I have a base level of is it better than a chat GPT agent mode or anthropics cloud code?

Yeah.

Or Gemini, CLI, whatever.

And so I started running these benchmarks a little bit.

And so I was very familiar with how good it could be.

But then I was like, oh, it isn't quite there.

I vibe coded some stuff on Opus 4 for sure.

But like, you know, it was like kind of interesting projects on the side.

It was really hard.

It took a lot of feedback.

They would 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 wise, whatever it's like no, just one shots 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 it 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'd done in the past.

Oh, I also tried Codex too before this, like like Windows 52 never really got it to work in the way seamlessly agentically.

Oh, of course.

This was recent.

Oh, no, no, no.

So, so, so this, my most recent, when I was like, oh man, the awakening, probably December 27th.

December 27th.

You know it to the day.

Something like that.

I'm thinking cause it's between the days and 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 then also there's two X usage limits.

Oh my God.

I miss those days.

But I mean, Now I'm addicted to fast stuff.

But look, I was playing around with these coding agents just like everyone else should or should in the space and like quad 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.

I was like, wow, it could just like one-shot stuff.

I have all these interesting ideas.

Is it Excel sheets primarily?

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 think...

Excel it's moderately okay at like, let's say, one shotting a basic financial model or like just taking 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 the business it's like, is your model, you know, being 5 more accurate, really going to ever make a good investment decision or not?

No, never, not once like no one's saying, Oh, yeah.

My estimate is always one cent more tighter than everyone else.

That's why I'm good at stocks.

No, it doesn't matter.

It's like Felsight is ridiculous.

Of course, everyone's like, I'm bullish because my EPS estimate is like 10% higher than the street.

And I'm like, oh, who cares?

Well, I mean, as you know, sell side.

If we're going to do this as like shots across the bow on sell side.

I mean look, one of the reasons why semi-analysis has such a successful business is because I think sell side as a concept is very broken.

If you're talking about waves and things that are changing sell side.

In a lot of ways, is this hereditary child of like, let's say, 30 or 40 years of banking where you had, you know, a company, go public?

So you needed someone to talk about it, to issue securities.

And so you're selling the stock.

You're literally selling the stock, but you have to be independent ish.

So your ratings buy, sell, hold.

One of the biggest sales you could do is like when you're when your company IPOs will talk about you so people know who you are.

That's the core original part of the sell side.

Right.

And the problem is like all the research kind of has this like really kind of fallen apart.

It's just not different.

A lot of banking regulations has changed.

And so, like the primary information process, it's like a 40 year old business model on its last legs.

And so, I mean, that's one of the reasons why Seminalis is so good.

It's because we are not focused on being a one cent EPS thing, which I would argue isn't exactly skill.

It's just mechanical maintenance.

We are really good at understanding when technology changes and how that impacts everything.

Right.

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 something like that, some bottleneck.

Being really right on the timing and the magnitude of those inflection points will make a huge difference in the stocks.

And so that's our business.

We're a research firm.

We're independent.

And we've had a really good hit rate.

And we care deeply about the technology.

Exactly.

Yeah.

You know I didn't mean to characterize you as, like 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, always wonder if it's like okay, it's like one, you have like deep understanding of the tech.

Two, maybe you're like sort of financially sort of literate.

But also three, there's like this, like X factor, that is like well, focus on things that matters, fuck everything else.

And I don't know what that is, but that obviously is the alpha.

Yeah, 100%.

That's always been the analyst PM conversation.

It's like, hey, you know, there really is only one or like three things that actually matter, right?

Find me those three things.

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 the 30s.

This case doesn't matter.

This one actually matters, because now you have a giant opportunity.

And so that's, that's what Yeah.

Yeah.

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 would call a laser.

You know, I can't remember what email is replacing, blah, blah, blah.

And you're like talking about all this stuff and their 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 things, 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.

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 to find 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 tasks that I was nailing and the tasks that it's still not great at.

Yeah, so let me specifically talk about my use case because, hey, I am still a stock guy.

I can't trade or do anything in semiconductor or AI world.

But, you know, I do still really enjoy stocks.

It's one of the reasons I'm passionate about it.

And it's probably my defining skill, what makes me good or bad at stocks, quote unquote.

You know, the people who are really like stocks, they're like lifers.

They just love this shit.

It's like an addiction, OK?

So 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.

That's why do you give cloud code?

Yeah.

Okay.

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, sure, fine, whatever.

And then also like everything you do is perfect.

I'm like okay well, like actually can we like make an investment framework for for my investment style and start to grade all this stuff and then like attack it and do stuff like that?

You can just do like iterative work.

And then I was like whoa whoa whoa, this is like a crazy useful tool that 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've been I've been doing this systematically for every aspect that I can think of.

Like, hey, now it's so much easy, easier.

Like I was actually perfect example is this is this chart, right?

Hey, cloud code is a really big deal.

Everything's one shotting.

I'm reading everyone going into psychosis like me at the same time on the Internet.

How do I actually know what's real and what's I wonder, right?

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, or onto your, your commit, as says Hey signed off with I'm like.

Well, why plug code?

Scraped me all the commits.

Right.

And you know what, lo and behold, it pretty much did like and it's like okay well, like I'm looking for this signature right here.

Copy paste was like. how would you systematically go about doing it?

Did like a big query pull for all the stuff pulls all the like every single day.

The API is relatively open.

And then I'm like, oh my God, let's see how much this is growing.

And it's like, okay, chart go up.

And you're like, how big is it as a percentage of GitHub?

You're like, chart go up.

It's a huge deal.

And I'm just like watching her.

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 biggest deal.

I love watching trends.

I love watching exponential trends.

And I've never seen one even remotely at this rate.

You would, you know, 4% in like two weeks or so.

Do you know about PR Arena?

It's a previous attempt prior to you.

But somehow they didn't talk about... They just talk about merge rates.

They don't plot it as nicely as you do.

Yeah well, and also you want to.

You asked the question of what is this as a percentage of GitHub, and this guy didn't.

Yeah, that's it.

Yeah.

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

Yeah.

But I thought the quad code, because I'm just trying to really, really, really focus on that.

So typically, yeah.

Well, and also you want to give an example.

Bro, I didn't make that chart.

Opus 4.5 did.

Or I think 4.6.

I'm like, hey, I want you to do it in this style.

This is the semi-analysis color scheme.

I like summarize books about visualization and like put it in.

Here are some style tips.

Yeah, here's some.

I don't even know, man.

It has like, I had it go read like 70 books or something.

I'm like, give me like, you know, the.

It's probably a waste.

Yeah.

No, no, you're finding it hard.

It is a waste.

Look, tokens are free.

The cost of doing this is nothing.

That's the part that's so amazing.

Yeah, yeah.

The cost of doing this is nothing.

The information gathering and synthesis is like hey, if it costs effectively the same doing 70 as three, who cares?

Right.

And so I like whatever.

And the answer, I'm like, oh, this is too many tokens.

You better like really summarize this into like 90 tokens or something like that.

A really basic whatever.

And then you have all the skill but like okay, now you can put all that into a skill of how to make charts in the semi-analysis format using any kind of data.

And then you can systematically just push this out again.

I'm like, hey, data analyst, please consider all the relationships you can generate information.

Like, I think it's that one was not chat GPT.

That was not generated.

That was not generated, which I hate, honestly.

I don't like that much.

That one as much as it doesn't have the guidelines.

Yeah.

And and so you can just that was that was generated.

And so you can.

Just what you can do is you just ask it to do, is like hey, here's all the dates that we have.

Can you like visually brainstorm with me a way to better represent this information?

It's like, yeah, actually, I'm going to generate you a timeline.

You can just do things.

And I mean, that is your catchphrase, right?

Yeah, that is my catchphrase right now.

You can just do things.

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

But like all information work is you know, I would argue, coding is a big subset of all information work.

I think there's a Brian Hobart tweet or something forever ago.

He's like you know coding and financial.

You know finance people actually are very like different types of abstraction.

But you know you are doing abstraction.

Excel is a ginormous abstraction.

You're building these relationships and you're describing what you think a financial thing is worth right.

I think coding is a little harder, 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?

The answer is it feels like a skill issue.

It makes errors on the margin, but you can kind of force it into like for me.

I love using rubrics, right?

Hey, I care about X, Y, Z. Out of 10, score this, and then you can really do multiple things.

It helps with the stochastic.

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 are done?

I actually have two versions of this.

You can pull all this stuff together, just run the prompt for the rubric or whatever.

Or you can do the task and the rubric.

It just depends on how you want to do it.

Yeah, because obviously if you put a task and a rubric, then it can iterate itself.

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

Yeah, exactly.

And well, in the other part of it too, yeah, it will iterate, but like...

So context doesn't matter.

I kind of like it to be separate, because the thing is it's like oh, it needs to be this, like fresh look at it.

You have to think of it kind of like it would perceive anything anywhere.

Right.

It just each context window is just opening it up.

And I think sometimes, if you have done, if you do it together, it commingles the information to the point where it becomes visible, biased or susceptible.

Opus 46, as you know, is like super sycophantic, like it loves to like say yes okay yeah, i'll do this for you.

Yeah, i think having it separate keeps it like keeps some of that drifts kind of away, and that's like one of the things that i've really.

Personally, i like the results better, but it's it's just complicated.

Like part of this is really weird, because I am 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, I just thank God someone said it.

This is a huge deal.

There's this like meme where it's like, these guys.

Well, do you see the meme?

It's like of mice of men.

And at the end of, you know, at the end of the book, I can't remember which character she was.

I know, I know.

Yeah.

So one character shoots the other guy and it's like some guy made a meme about it being like oh, this is after your, after your cod code is garbled.

You know five million tokens.

You're like, OK, it's time to put you down because the context draw is huge.

So, yeah, this yeah, this is an example where.

So what are your compact practices?

Do you sort of aggressively compact manually or?

So I personally, with the new 1 mil, I feel like I try to do it all in one compact window.

I'm not doing ginormous.

The 1 mil is very new, right?

Yeah, 1 mil is very new.

But it's a big deal too.

Because.

Yeah.

Because your skills and whatever your cloud MD is a percentage of one 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 big, you know, the one million.

You just want a really high quality product. a project within that, that's the best, in my opinion.

Compacts just kind of start the compression of the noise.

So yeah.

Yeah.

Mentioning sub-agents and multicharts.

So first of all, I wanted to give a shout out to this thing from Anthopic Research, where they were like here's our production traffic.

And they, they did a, did a report that was kind of like their equivalent of the meter chart.

And there's a lot of people saying that, oh you should.

You know, software engineering has PMF.

But here's the next list of everything else.

But what if they're all also just software engineering, right?

Like, software engineering is like 50 right now, but there's nothing something from continuing to go to 80.

I think maybe what's going to happen, this is like maybe a giant dream take.

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

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, like like chips is upstream and then it's AI and then it's software engineering is all the extension of that same compute hierarchy.

And I think the, like you know, teaching where machine and code kind of, or in the world intermingle right now is code.

And so that's just going to be the bleeding language that's used to figure out everything else.

That's my belief.

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

This is like Excel.

Cloud for Excel is much worse than cloud code using Python to use the Excel skills to then deposit into.

It's all a lot much worse.

It's much worse.

Even when all the work they're doing.

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.

It's like a backwards compatibility thing where it does work because lms are like relatively generalizable like this.

But why bother?

Because that same abstraction of information on excel.

It's just in that, because it's human formatted for us to understand.

And i think that that's the important distinction.

All of this information stuff, all this software stuff is just to be consumed by humans.

It doesn't matter 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 PowerPoint or Excel.

It will just deposit the matplotlib in a chart to me in an image.

Fine.

Are you trying to use matplotlib?

Yeah.

Wow.

Why?

You know, it's better understanding that code.

Yeah, yeah, yeah.

So why ever make a chart again?

Yeah.

If it's better.

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

Yeah.

I don't care that much about.

I 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 increases the speed of information, that matters a lot.

Yeah.

And so I think we're much more.

So pretty much the new charts will outweigh the old charts, because it'll just grow.

Yeah.

So yeah, I think it is a little inconsistent.

We have the same watermarking.

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

Well, the first thing this looks reminds me of is Bloomberg.

I was like, you guys are just like, you know, becoming Bloomberg.

Which is a nice... That's the beginning of your company.

Yeah.

A 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 sort of cloud code shills in the world, which is sub-agents and agent swarms.

I don't know if you've tried...

I have tried them.

Pick either one, whatever you want.

I have a controversial opinion that quad does not do RL on the agent's forms or agent team.

Yeah, it's just an experiment.

It's just an experiment.

Thank you.

No, not there.

Exactly.

Because it's just via prompt and it's actually very bad.

I think sub-agents are okay because they usually have a quad MD to go do whatever.

But the agent team is horrible.

It's actually really bad.

We can't knock it because it's experimental.

Yeah, no, no.

What do you try it on?

It was like some big data analysis of many, many different companies with different KPIs into a dashboard all at once.

I was like, hey, can you just make this all whatever, split up the teams?

You know, speaking of that, though, you say that, but can we do one on...

Agent Swarm is actually good.

I have also tried that.

That is actually really good.

So I did some like oh, example of things I was never available to me like internal benchmarking of these models and be like hey, here's a set of problems I'd like you to do 20 times.

Can you do them?

And then I can measure the performance between them and then like do qualitative.

But like, what's the difference between X and Y?

Yeah.

That was completely out of the hands of me, a normal guy, like three months ago.

Now it is completely available to me.

That's awesome.

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 partially automated.

And so...

I mean, my experience is the 2.5 swarm actually improves the model's performance meaningfully.

The agent team makes it meaningfully worse because there's clearly not RL done.

So it isn't context aware of what's the best thing to be done.

And yeah, so 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 meaningless.

They had some post about how they did stuff.

Yeah.

There's a bunch of RL for this.

And I tried it myself.

I thought it was pretty.

It's cute how they do all these little games and stuff, yeah yeah.

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

I think it's like a 16 node of h100s okay and you're just like dang.

So you and I are not gonna be running and this is just to run and I'm sure there's concurrency available.

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

They're there, 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 to hey, how do you get these like this one agent to essentially get a verified whatever, put it into a bigger process and do more information work?

That that's the next thing.

And it's important to have context windows that that don't garble up into random stuff and is able to do just like good enough with token efficiency.

I think is a huge part of that.

So yeah, 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 showed better.

No, definitely better.

But the Kimmy 25 tells you that this is already boom, perfectly great new place to do more experiments, 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.

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, like the, the sort of AI tigers out of China were were DeepSeek and Quen.

And then you were like, well, who are these like Kimmy guys?

And, and these, these sort of newer names, like I guess Minimax as well.

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

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

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

Yeah, yeah.

They have a way to productize it, but it's kind of like the Gemini version.

They have so much stuff to do elsewhere, right?

Yeah, yeah.

But yeah, Kimmy's pretty interesting.

They're pushing so hard.

They got everything.

I know.

They got Kimmy Manus, Kimmy Claw.

Kimmy Claw.

Yeah, I know, Kimmy Claw.

I haven't, yeah.

Dude, I was going to say, have you messed around with OpenClaw?

Because I did, I remember, what was it first called?

Clawbot.

Clawbot, yeah.

Dude, I was going to say, it was really, really euphoric.

I was like having it read all my emails and my calendar and do all this stuff.

And I was like, wait, 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, cloud code psychosis is good enough for me at this point.

I mean, so what I do is I just have multiple emails, right?

And there's, there's a safer email to give to bots and I can let it use that.

And if it impresses me, then I can upgrade it.

But cloud bot didn't impress me at the time.

I'm going to be honest with you.

I wasn't impressed either.

That was the reason why people were freaking out about this mold book.

I was like, bro, have you actually used this shit?

Because it's not even right now on cod code in a relatively focused terminal.

It will be like, oh, blah, blah, blah.

I'm like, dude, in the dot EMV, there is an like.

In the env.

There is an API I told you to use for this subcase of problems and it's in your CloudMD.

Please focus up.

It still is making mistakes.

This is not truly AGI, and there is harness.

You still have to wrangle this thing, but it's not 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.

Yeah.

So I use our Discord as basically, like, a way to just bring information in and out.

I saw this too where basically, a lot of people are just setting up things that they could have done in Zapier with Cloudbot, because they're like well, now I'm AI-pilled.

But actually, they just done it more securely with Zapier.

Like, I think it's kind of interesting.

I guess I do think it's kind of interesting, but I think there is.

But the difference, though, is Zapier.

I mean, I remember I've tried to use Zapier before.

Yeah.

And it's also not very.

It's not also not very good.

The difference, though, is like and that's OK.

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

Right.

Cloud bot, the cloud code, cloud bot, 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 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 Codcode did it in like, you know, four and a half minutes.

And that's good enough for me.

You know, that's a faster achievement.

And so like it's totally okay that 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.

I think there's also like an innovator's dilemma thing where zapier, as a pre-existing business, had this view of the world of automations as like very strict sort of on rails workflow type things that their giant user base already uses.

They couldn't like really pivot that much, yeah.

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

You end up becoming, you know, the box will control you.

You are... It's your golden handcuffs.

Yeah, it's just like your cage, you know?

You're going to act like how you are in the cage.

And so, yeah, that sucks for... Honestly, I feel like that sucks for some people.

The framing I have is, like, your prior is becoming a prisoner...

Ooh, that's pretty good.

That's pretty good.

Your priors become, yeah.

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

You should, you should.

Your priors become your prison.

I like that a lot.

Coming back to Cloud Coach, I also want to make this like the sort of Cloud Coach.

No, no, no.

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 about Kibbe.

Yeah.

So do you use hooks?

Give me the Doug O'Loughlin cloud code setup.

I had just 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 all work in progress as well to have APIs for some of the semi-analysis information out as well.

And so that way we have an internal server that is accessed by people with an API, so that all the semi-analysis researchers are able to hit some basic level of context.

Because I think the context is really what matters.

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

I think hooks are very underrated, right?

Yeah, I do think... Because you can do, like, a Ralph loop just with a hook.

Yeah, I feel like I underutilize hooks.

I think that is true, but I do run some version of them on, like, skill calls.

Like, hey, on this, then you have to start pulling all this stuff.

But I think in the beginning I tried to do all this like hook stuff and like compounded stuff, like that.

And I found that, like you know, the gas town, Ralph loop era it's like it is a sign of what will come, but I just don't think there's enough fidelity to like make crazy multi turn.

Something happens.

So like okay, actually less is more, try to have like a strong set of smaller skills with a good amount of context information to be pulled in.

And then, at the beginning of every session, ask and focus on what you want to do so that, like it prompts the like, not like you know, a cloud within a cloud, whatever.

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 d-ram prices.

Okay, I'm going to first start by gathering as much information as possible from all this stuff.

Blah blah, blah.

And then we're going to fine tune it, evaluate which ones we're going to do.

I chose Kronos 2 because of covariates, blah, blah, blah, blah.

Try to set this all project up.

We'll make it a Vercel dashboard internally for semi-analysis.

Maybe we'll External if we want, if it's a good enough product.

Okay.

So then it like does all this stuff.

And then I just like start planning away.

Hey, can you go research?

So here's the search API, Serpa or X 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?

Showed me which GPU, whatever we're renting on an hourly basis.

And so, yeah, we just pull all this stuff together and we fine tune it.

And I'm like, okay, cool.

How does 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 LN is probably not going to work.

Unfortunately.

Unfortunately, the... You said it was because of regimes or something else?

I think it was because of regimes.

Yeah, there's no way.

This regime was so 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 like stock trading stuff games with LLMs.

They have no idea they've never studied finance and like past does predict the future a lot until something fundamental change.

And like the macro shifts and like risk on versus risk off.

They've never heard those terms.

I had to explain it to people at Cognition and like Yeah, the rules invert.

Like completely invert.

Like what used to work is exactly the opposite of what you need to do when you have a regime change.

Exactly.

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

Right.

Which reduces the sample size.

Yeah, which reduces your sample size.

And so then, at the end of the day, you end up being like well, it kind of just like, I guess it's.

Here's some heuristics.

Good luck, have fun, right?

Here's your checklist to see it might be over, but you really don't know anything until then.

So, but like, okay, an example of where this project was helpful and was 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.

I did burn quite a few tokens.

But I do happen to have, after all this information synthesis and analysis, all of the memory, prices of everything I could possibly find, plus the things behind API that we paid for, plus enhanced data sources.

And I have all the covariates.

So like, hey, WFE, what was the consumer sentiment, every macro thing of all time.

And you know what's really interesting is I am 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 that 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 grok 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 brought through all I'm like, oh, what was GDP this grow?

What this year?

What was all this stuff?

And you have to like make all this giant sheet to come to whatever and then make the narratives.

No, none of that shit, dude.

I mean, this is like too much information to gather.

It's like a lifetime of work.

It's like a PhD project.

I did it in a day, two days.

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

Yeah, there is some slop.

I definitely agree with the slop, you know.

So I think of it once again.

So right now, by the way, that's also existential for you guys.

If you get caught doing like putting out some slop to your clients right, like you have to yep at one point, be like extremely AI pilled and like you know yeah, number one in the world at applying AI to your productivity, great.

But also, you got to, You have to.

So I think the thing that's really interesting is that this whole thing is a game of hygiene.

Now,

Yeah.

Because I think it's like This is really hard.

I think about it all the time.

I feel very comfortable with doing all this work because the thing is my at the end of the day, 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 gotten me in here is actually like tons and tons and tons of information, set up scenarios and like pattern recognition.

Right.

But yeah, you're right.

This.

This crap makes mistakes all the time.

All the time.

It is still just like a like.

I think of it once again.

It's 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 l1s do it right.

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

It better happen if you're spending a trillion dollars and there's not meta-level learning.

But for me in our firm...

That massively amplifies everyone who is an expert, right?

And we are a firm filled with experts.

And so it's this hard part where I wonder if new people we will be less lenient in terms of like, how much AI tools Were you like junior or new to the firm?

Junior.

Oh.

Junior to the firm.

Yeah.

Or junior.

And, like, because, like, you have to still do something.

You can't just, like, slop it up.

It's very obvious to me what it's slopped, right?

When it's slopped and there's no cognition, then it's, like, whatever.

The artisanal last 5% is, like, that really matters.

Yeah.

But for me...

I know inherently what the 5% is.

I can like write it away with some really easy heuristics and time and like be like okay well, this is the last 5 you fix.

This is what I believe.

Just fucking 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.

You just compound all this noise and it becomes just like garbled, just like all contexts rot.

But in terms of like the capability, that is over hit.

Like you know, the human CPU in this agentic swarm is very, very powerful now.

Yeah.

You know, a huge, huge, huge multiplier of what you're able to do.

And for me, that was enough to be like, I feel AGI pilled, honestly, because if 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 take or completely shift a lot of the information work?

Yes, 100%.

Data analysis is a perfect example.

Hey, every quarter.

I want you to just find me some examples of some information that might be interesting.

I just can't imagine if I was an entry-level worker doing data analysis that...

A 22 year old an average 22 year old would 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 45 capability, enough like that, that we hit some level agentically where it seems to work and do bigger information work.

That's when I'm like okay yeah, this does change everything.

And so, yeah, there's all kinds of mistakes.

It's a new level of hygiene that we have to do.

You're going to have to understand what the absolutely of agentic work is back to you.

I catch it making errors all the time.

It doesn't always pull skills.

You can definitely tell context windows.

It gets dumber over time.

It's not AGI today, but it can do these crazy long tasks.

And as long as you finish it at the end and deposit it as information work, that's very valuable.

Yeah.

Amazing.

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

By the way, transistor radio, amazing for like understanding like what your world is like.

Yeah.

Are you also cloud co-pilling your analysts and your...

You know, on the other side.

I've definitely clog code pilled the analysts.

Everyone in the New York office, I'm like, must try it.

I like really tried to like.

I mean, not your, like not the semi-analysts, your customers and all that.

My perception is they don't adopt any of this stuff.

Okay, so yes and no.

Some people are interested, but you have to remember it's relatively more conservative.

But, I think, but if you ask any analyst if they're using AI, every single one of them will tell you yes, I use it every single day.

Of course, how could I not?

This is like a vital skill.

And so the basic inference that I'm doing is I am a bleeding edge adopter.

I'm a relatively smart dude who knows what he's doing and if a tool is used for or not.

And I've evaluated the tool and I'm like wow, this is an amazing tool that I literally like pry it out of my dead fucking, cold hands okay.

I'm like this, even if it's like makes mistakes, I will be using this for all kinds of work forever.

Then I look around to everyone else and be like.

Most of these guys are enough like me that if they have an opportunity and an edge, they will obviously apply it.

And they look at this tool and they start to use it.

If they start to use it in their 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 what we'll see in the 24 month view, it will be a base level, I thing.

I think cloud code, co-work, whatever is going to be a base level of all information work very soon.

And you see one.

My friend was telling me how his portfolio manager found co-work and he's 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 35 or 4 for me, where there's that first time where you're like okay, I know I made some shit up, but like this is better than like if I went for hours searching, putting information together, it can.

And then Also it's 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 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 cloud code moment, I think.

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

And I think we need a version of this that is the cloud code.

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

Exactly.

It just strips everything like right or something.

Yeah.

But also, 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.

Yes.

Whereas as a human, you get one turn.

You get one turn.

But with Clockwork, maybe you get three turns.

And the sort of review process is the thinking.

Yeah.

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

Yeah, I think of it as hygiene.

The thing that's like really going to be painful though, is like a lot of my expert opinion has been built by, like You know, it's like pre-phones and not right.

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

Okay.

Like, you know, the attention spans are really bad, all this stuff.

Like, I don't, I read this like really sad thing.

We like, oh, we're getting dumber or something.

First generation.

I don't know.

I'm not going to, maybe that's like.

Did you see the Coinbase earnings?

Yeah, I saw the Coinbase earnings.

So, like you have this thing where it's like OK and it's cute and all, but like it's such an addictive technology that, like I feel very grateful that I'm like well, I understand what I'm doing, have this history of doing stuff and able to apply a tool.

But like people who are riding this curve, it's going to be very dangerous.

It's like giving everyone.

Yeah, that's so funny.

I think you should just do that.

Well, we do.

We do with some of the semi-analysis memes, you know.

And the thing is, you say some of this brain rot is like so bad, which is terrible.

But some of it is also, like you know, it is hitting some attention mechanism in my deep, primordial monkey brain.

Stimming you.

Yeah, it's stimming me.

And you're like, you know, I can't look away. from the subway servers.

So... You couldn't look away.

I had to pause it.

Yeah.

I literally Well hey, have you ever been at a bar when they play these weird?

They'll be like tiktok videos, for lack of whatever, and you just watch.

There's tiktok bars in new york not tiktok bars, not tiktok.

Okay, 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 clock code payload is very powerful for me.

I believe it will change how it all works.

It'll shift all of that over massively, the chart.

But it's just really weird, because if you didn't pay any 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 makes that loop work is because once upon a time you did that and you could make that to be like yeah idiot, you're not thinking about this problem in this way.

You're missing 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 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's through learned experience.

And I wonder if we're just reviewing, we won't be building and embedding those assumptions to understand judgment.

Right, right.

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

Yeah yeah, I think that's that is.

That is a danger.

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

So I think that that's it's gonna be really interesting.

I mean, have you tried like so?

So I mean, there's the way to model the sort of meta learning as element is like once a night you do a batch job of like look over everything I've done, like extract some learnings you know, and OpenClaw.

I think one of the interesting things I really liked about it was this heartbeat.

And I'm like, people aren't like excited enough about this.

Cause like well, this is the first instance where like, the agents are just always on, like always living, always reflecting.

Yeah.

I mean, I think, yeah, that's a good way to put it.

Yeah.

And so, like that's the powerful thing about all this stuff.

Is that like okay yes, we know that the contact, like it gets garbled.

We know that open, open claw doesn't always do everything you ask to ask it to do initially, but you can see the design patterns like the heartbeat.

Md is a perfect example.

You can see the, the design patterns where it's like well, you know, Is all of our tasks every single day actually us having this like genius thing.

Or do we like sit down in a single session, finish a single project and get up and get some coffee, then come back?

If it's that and you can just fuck, you can make the heartbeatmd consider the like the session to session and like hey, meta learnings, all this stuff.

And it's only specialized and focused on one project. 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 feels like aging.

Like that's effectively good enough to do a huge information hack and have enough context and be able to fetch it.

And maybe like there'd be some verification to make sure it doesn't just totally mess it up.

But that to me feels like a design pattern that you can build something on.

And so that's the vibe is that We've hit some capability that 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 wouldn't have put myself as AGI Pilled.

I think 4.5 is like actually...

I think my own timelines have moved up a lot.

Yeah.

Are you guys watching GDP Val?

I, to the best I can, but I feel like I'm mostly just trying to No, no, no.

To me when GDPVal came out, so I'll just GDPVal is like, basically a like a broader three bench, let's call it where it's like, applied on every profession.

That is white collar that you can model.

And it is above like something like two 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?

Like where 50% is parity with industry expert.

Yeah.

Coin flip.

Exactly.

So like you can see the nice increase from 4.0 to Opus 4.1.

And since then, obviously 5.2 and Opus 4.5 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?

Yeah.

And so I think the problem though, I would say that that is the definition of AGI.

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

We're moving to goalposts.

Yeah, we're moving to goalposts.

But I'm like bro, the goalposts, like I mean we'll see if this is actually the machine.

God and Shogoth will come and talk to us and vibrate on our same.

But what if I do think so?

Okay.

I don't, I mean, honest with you, I'm very open.

I will change my mind often.

I'm not, this is not something I feel intuitive in my gut today.

Maybe it's the next, next, next thing.

But when it comes to like the, the, the GDP valve version of this, yes.

Do white-collar work.

White-collar work.

Which is most of the time.

Very boring.

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 99-1 thing.

I don't know if it'll be quite that drastic or whatever.

Maybe, you know, everyone's just doing leisure.

So far, my experience is everyone just works harder.

It's been my experience.

But it just feels like a massive moment's happened.

Like the steam engine's invented.

And you know, the trains are here and everything's going to change in knowledge work.

And...

It's kind of crazy.

There's this sort of economic cycle from my macro days that 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 economy starts out majority agriculture.

Then it discovers like manufacturing.

Then it discovers white collar work.

Then it builds a very mature financial sector.

And these are like a layer cake, all declining over time.

The new thing is increasing.

So my theory is there's this fifth layer that has to open up.

That starts to happen, because I do fundamentally believe we'll just invent new work.

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 warmest place.

Humans are in every latitude.

That's in a physical sense.

But I think we're going to find a way to make utilization go up.

But we'll invent more work for sure.

But I think the thing that's crazy is just like, Things change so quickly.

And that five to 10 year period, like 10 year gap can be drastic and crazy.

And that's just societally wild.

And it's happening in our lifetimes.

It's happening in our lifetimes.

Like it's like happening like right now.

It's really crazy.

It's like very, and like, so this is like a complete side task.

Yeah, yeah, yeah.

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

That's like my pet.

Yeah, why is it not showing up in GDP yet, right?

So there's going to be, you know, some people are going to be like, oh, you know, the facts.

The facts, the internet same thing, information transfer, whatever.

I think.

I'm actually scared for a third worst thing, which is like now.

Now, this is a complete crackpot theory.

Please don't hold me to this internet.

But what if AI is massively, massively deflationary?

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

My spiciest take is I feel like GDP itself is going to be very, very challenged by AI, because information work.

Yeah.

So how we, how we capture it effectively, is all of an economic good.

And then the service hours, divided by hours.

OK, so there isn't like a widget to widget difference.

But in theory, if we could.

Break all of information work down into units.

We're going to have a lot more information work for sure.

Like more work will be done.

I don't know what the value that's going to be.

Is it going to be so much increase in supply?

It's deflationary.

That seems to be like a real concern.

It's possible yeah, and then we'll figure out how to use it, but like there may be a great depression of ai yeah where, like we figure it out?

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

Okay, my favorite capital cycle it's on fab.

Yeah okay i, It's like railroad.

It's about all the railroad stuff over time.

Okay.

Pretty much because we're like, everyone was first looking for the internet.

We've well massively passed the internet in terms of the absolute size of the build out.

It's not even close.

What numbers are you thinking of?

What's good?

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

And I think we are well past like we like whatever this year and it's cumulative, right?

We will well past that.

I think railroads, the reason why it's so interesting is because honestly, it's way crazier.

But but part of the problem and craziness of it too, is like Railroad was literally like one of the first added layers of the layer cake, if you think about it.

Before it was agriculture and railroad was like okay well, how do we move this agriculture around faster?

And then banking got.

I kid you not like.

One of my big takeaways is banking effectively got invented by railroads.

Oh, because there's no need to finance it.

Yeah.

So much money was needed that like effectively 85 of all paper or whatever was essentially just railroad debt.

Yeah.

One of my favorite anecdotes was before there was a federal bank, a federal reserve.

Andrew Carnegie was the federal reserve.

Yes.

Yeah.

There were individuals.

Yes.

Yeah.

And so all this stuff.

So it's like the whole thing.

I kind of did some work on the Gilded Age, all this stuff.

But, like my takeaway is like that was a really interesting cycle because it was so big and took so long to deploy.

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

Actually, there's three boom busts.

I don't know if it'll be quite that long.

All the cycles kind of collapse.

Yeah.

Because, you know, information moves around faster.

Exactly.

Yeah.

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

That's my vibe.

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 world was invented out of it.

That's like my favorite analogy for this because, like I think it was like GDP percentage of CapEx each year were like high single digits sustained for like 10 years.

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

Did you do percent of GDP?

Because I think that's the way you make it convertible.

Stargate itself, 2% of US GDP.

And I mean, it's going to go up.

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 total capex it was 48 of GNP and 25 of total gross fixed capital investment.

Okay.

So 25% of investment every year, and 5% of GNP.

I think we're there.

Stargate Plus and Tropic Plus, whatever.

Yeah, we're right there.

XAI.

Yeah, so we're at a railroad build-up.

Which is like, at one point, but the thing is crazy.

We should exceed it.

Probably, yeah.

Not probably, we should.

Okay.

This is bigger.

Yeah.

I would like to say, yeah, sure.

Yeah, we will do it.

I'm worried.

I'm like, dude, we're going to get all the money.

That's like such a pedestrian concern.

Yeah.

It's not a pedestrian.

I mean, that's what happens every capital city.

We must hands in the Middle East.

We'll flip the thing.

We must, we must.

This happens every single time.

That's the reason why the bubbles happen, right?

It's like, we essentially get so big where it's like, this must be built.

It doesn't matter the price.

And then all of a sudden we look at it was like, oh, that was a steep ass price.

But I think I mean.

The thing I think about this is like how I think about the big picture is there is a demand curve and a supply curve and we have no idea when they cross.

They will cross one day.

And every single year, the demand, we're finding that demand curve.

And then the supply curve, we're just like, we're doing our best to deploy it.

And I think for me, like, I don't know when that number is.

I'm not.

I don't want to say a number go up forever, because I feel like that's like intellectually dishonest.

But Quad Code for me is the first time where I'm like, and we're bringing it all back together where you're like demand go up so much.

I am now guzzling as an individual.

Like, for example, I'm off max.

It's not enough.

It's not even anywhere near enough.

I mean, some people buy like five maxes.

Yeah.

So I'm on fast...

I'm on Fasted with $1 million on API, which is, that is like an addiction level, if any sense.

But yeah, I really think it's the first time where I'm like okay well actually, how much is this worth to me on a yearly basis?

I think it's like $20,000 to $30,000 easily, if not more.

Like, I don't understand, like, what's the, like, I can't price it.

I have no idea of the elasticity.

Yeah, you pay for a perfectly compliant junior analyst.

Yeah.

Right.

And so what's that cost?

Like 90K.

That's able to work in parallel.

Yeah.

Like you can have 100 of them.

It's kind of crazy.

Yeah.

So it's.

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

Yeah.

100%.

Which like, I mean, okay, like, you know, skill issues, like it's your fault.

But no, like we have to learn how to do this.

It's three months old.

Exactly.

That's the correct way to put it.

It's like it was definitely a skill issue that you didn't know how to get like your settings on your iPhone to work.

You know, at one point 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.

I think it's just about a time.

It's a bit of time and it's like kind of interesting because you're watching.

I mean it's cool is that if you're like on this absolute bleeding edge, you get to see the design patterns like blossom in real time.

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

He's, like, one of the most interesting intelligent people in semi-analysis.

And he talks about how... Who is it?

Tanj.

Okay, so, like, he said this, like...

We had the conversation one time he was talking about early internet, how it wasn't actually sure if the browser was going to win.

It was like a remote web file service.

Some people thought well, I'm just going to reach and play with someone else's web files remotely right.

Who knows, right?

And that kind of is a remote web file.

Who the hell knows?

They were design pattern searching back then.

And I think we're at that again, where all the design patterns are open.

And it's like really interesting because there's many different ways this could go.

And we're 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 will disrupt all everything else.

Who the hell knows?

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

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 Yagi from Gastown talk about how 2026 would be the year IDE died.

And I was like Two weeks ago, three weeks ago, I recently was like shit, he's absolutely fucking right.

It's over.

I'm really wondering, too, because, like, ID... 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 ID 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.

Software is just going to be first.

But the year of Excel is dead for finances.

Excel is the IDE for analysts.

Bloomberg is the IDE for analysts.

I believe every one of these IDEs are done.

It's dead over and over and dead.

I just think it's, why?

Why?

It doesn't like, just imagine the concept of you.

Like I remember when I learned Bloomberg, I had to like watch videos to learn about all the random subfolders and 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, crazy to think about.

That is like.

That is like horse and buggy.

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

I think all of that is dead and like.

This is why i'm like.

My spiciest take of all is like microsoft is a lot to lose.

I think they have the most to lose of everyone.

Because Excel is a human IDE for information work that's generalizable.

So is PowerPoint, so is email.

Those are the base core level of abstraction that decided to be broadly general.

But I just don't think that matters anymore.

I think cloud code or coworker or whatever is going to be the year that, like it, will destroy all of that.

All that information works that that where you sat every single year, it's over.

I think that's 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.

I will never make a chart in Excel again.

I actually will.

Yeah.

It's hard to let go because I have so much like ingrained knowledge of like manipulating 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.

Central decay office.

I remember.

Yeah, yeah.

No, no.

Oh, yeah, yeah.

You were one of the few.

I was a Centio customer.

You were a Centio customer?

I was a Centio customer that got rolled in, dude.

I remember how dare they acquire Centio.

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 Slack.

And it's the journalism, which is amazing.

And then it's the data feeds.

It's actually not really the UI.

Yeah.

But 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 interconnected, which I would argue that I am at some analysis.

Like, for example, I'm trying my absolute best to just rip Bloomberg out.

We're going to fax it API.

Like, oh, yeah.

All in API with a cloud code is my belief in the future.

Hey, verifiable data source that you trust.

Hey, scale up.

For you guys, you can do it.

For traders, we're so... No way.

I understand.

Like there's an information network that's like outside of this.

And you do deals in IB, right?

Which are tracked by the regulators.

Yep, 100%.

It's totally, I completely agree.

But as an analyst, yes.

But as an analyst, yeah.

And so like, but I just think that like okay, that doesn't really so.

You're right, the core cash flow Cal thing will continue onward.

But like each iteration of this AI thing, I was like yeah, I'm still going to be using Bloomberg, right?

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

The IBs.

My utils of marginal value from IB is now outweighed by how clunky this is and I want to just make some charts.

And so that's- Immediately you save $10K to $20K for switching down.

Yeah.

There you go.

It's amazing.

By the way, what was your cod code end of year prediction?

25?

Yeah.

I want you to know, I sandbagged the Everloving show.

Oh, okay.

I just believe 25 is very... Like, I... Like, it's like a...

The rate it's on is like whatever, 50 or something like that.

But I think I feel I wanted to give a 95 confidence interval.

I think 25 is within the 95 confidence interval.

Sure.

So it's between 25 and 50.

Something like that.

Yeah.

It's just absurd, but you know, it could also be codex.

Are you watching?

Yeah.

So, so to be clear, I mean, actually even willing to comment on that.

Cause like, I know we've done a lot of shit of being codex haters.

Yeah.

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

Yeah, I think together.

Yeah, we're watching Codex.

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

5.3, I think.

So we had the whole thing.

Because I wrote most of the articles.

I was like, oh, token efficiency, the context rattle.

Is it the same one?

Yeah, it's in the bottom.

It's in the paid section.

Okay.

But like TLDR, I was like, well, you know, the reason why Cloud Code is so good...

Anthropic is so good is because all of this token efficiency, the token efficiency is better than chat GPT, all this stuff blah blah blah, blah.

And then like 5.3 codex came out and it's like, yeah, that, that completely doesn't matter anymore.

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

I really think 5.3 codex is awesome in, in coding though, but you can watch it.

Like.

The reason why I like Opus 46 so much is because when i'm using it, i'm using it for like coding in is the way i interact with it, but i'm using it for broad, generalized information work right.

But i think the difference is codex wants to code because it's rl to be so good at coding to win on sweet bench 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 you can give it an API or whatever.

But it's, like, great.

I'm creating a piece of scraping software to go look at these websites.

I was like, no, no, no, no.

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

It's like, okay, great.

I'm still like it's so coding pilled on the URL that I think it isn't generalizable in the way that that 46 is where it's like oh, I could have it.

I could have it make some rubric or do some research or do something like that versus Codex.

It's very, it's very coding.

Codex is coding pilled and so that's what.

But i am very optimistic actually on codex and we do track quite a bit i, you can.

They have a meaningful amount of thing share.

You could see the bloomberry, they have the, the chart, bloomberrycom.

So the cloud code definitely is in the lead, but i think the part of it too is like the, the like to like, comparison.

There's a ratio of codex that's not available because it doesn't sign off every commit.

It does sign off on pull requests.

That ratio is much closer.

So all the open area people like Rune will tell like, well, we're not accounting for it.

Yes, we didn't account for it, but like...

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 53 is amazing.

Like it's very coding code.

That's when it flips over.

And yeah, look at the other players here.

It's just like...

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

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

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

Okay, that's a bubble?

Yeah.

Yeah, that's the CSF bubble?

Yeah, yeah.

Like, 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.

What's crazy is Cloud Code has a ratio.

Their attribution of Cloud Code and ARR is 2.5.

Yes.

So that, on this, the daily install counts, right, is an order.

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

Yeah, I know, I know.

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

Yeah, you're right, you're right.

CLI, NPM, download is another way to track it.

But I think they have their own installer now.

Anyways.

All in all, definitely heard, understand.

It's very hard for us to actually track it, but like... I'm not criticizing.

I'm just like, I think Codex, a big thing I'm watching is, well, is Codex back?

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

Yeah.

Okay.

So I have some, not skepticism, just because they have such a big...

ChatGPT portal that could be, like, TriAtlas.

Like, the modal that pops up can really move big users.

Like they're not quite a Googlecom in terms of having so much ability to like, siphon off users off.

But I wonder, like, the like-to-like.

But that's, like, my skepticism.

I have an answer for that.

Alexander Ambarikos was just on the LennyPod saying that they actually haven't invested enough in the web experience.

I think the attribution for that is zero.

Okay, yeah.

I guess I just saw a modal be like oh, try code, I mean, but a modal isn't.

And to be clear, Codex in Mac is great.

I'm actually... Yeah, yeah.

They actually made the app.

The app launch.

The app launch is actually pretty good.

So yeah, and I think...

I'm pretty bullish that honestly, especially for coding.

Cause it's like very coding code.

I just can't get it to work as well for non coding stuff.

Then my, you know, you use conductor.

No, I've not used Conductor.

Oh, okay.

I thought I heard you say on a podcast.

No, I've not used Conductor.

So basically their argument for any first-party app is that they're only going to prefer their own first-party games.

Yes, 100%.

They're already doing it.

I feel like this is how they're going to differentiate, right?

They're going to...

Well 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 clean superset.

No, in theory.

Yeah.

But, but I mean, this is like, okay, so, so, so then you can argue, this is the clean super set.

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 all the money and whatever.

Like, I just think my favorite one is like platform as a service.

If you remember that one, it was like infrastructure as a service, platform as a service, SaaS software as a service.

And I'm like, oh, this platform as a service.

And it's like, it always just ends up being in the middle.

So it just gets it even by one or the other.

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 like to have them review each other.

Having them yell at each other is really great.

I might actually try this soon.

I haven't used Conductor personally.

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

Yeah.

And this is, as a former cloud analyst, very typical of like do you want to 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 everything.

But if you go all in one cloud, you can exploit the sort of minor features of everything.

And, you know, the... it makes a market and there's no right answer for everyone.

Exactly.

Yeah.

That's one of those things where even the really small percentages in AI still really matter because they're huge and people are very happy, very productive, play money.

Okay, it's good to be an analyst in the space because it's fun to keep up with it, right?

Like, I agree.

Like, I think everything, everything.

We like the horse race.

Yeah, I like the horse race.

Number one, number two.

Ooh.

Yeah, yeah.

Yeah, no, no, I know.

But then you have to, your brain also has to be like, number two is really big too.

And then I just think, like for me, someone who likes the history of all this, like likes history of innovation and competition and disruption and stuff

Likes new technology.

It's like a very fun time to be following the stuff all together.

Tech during the like 2017 and 2020 years.

So boring.

Yeah.

At least for me.

Anyway, I thought it was pretty boring too.

Yeah.

Sorry.

I interrupted you in mid.

No, no.

I remember what I was talking about, right?

It's just a fun time.

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

Okay, I wanted to transition to a little bit of a spicy thing where you were on TBPN and their title that they chose for you was Douglas Loughlin thinks Microsoft is out of AI.

And...

Did you not see this?

Okay, so I wouldn't say out of AI.

Okay, so I didn't watch it.

I never rewatched these things.

Okay, so how I think about it, But you said things like Microsoft is scaling back investment.

So it was the previous conversation I was talking about.

Yes.

How Microsoft has the most to lose.

They had the most to lose of everyone in the entire world.

They're the software company.

The horizontal software company.

Yeah, exactly.

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

Okay.

No, like I cannot paint a bigger target.

Okay.

I cannot paint a bigger target.

Salesforce.

Yeah.

Okay.

That's another two.

Microsoft is automatically bigger than 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 quick bait title.

But the problem is the Azure business with OpenAI, right?

You're essentially renting barbarians at the gate.

You're like, you know, this is ancient Rome and you're like, hey, we need some extra guys.

So we're going to we're going to pay money for these barbarians to guard.

The Golden Army.

Exactly.

They're in Game of Thrones.

Yeah, yeah.

The Golden Army.

And the problem is like each year they become more powerful.

And then at some point they're just like, you know, we could just like scale these shitty walls.

So that's the problem is the wall and the moats.

Every year are getting more dilapidated as they continue to rent GPUs to the barbarians.

So it's just like Google, Yahoo again.

Yeah, it is exactly like that.

And so it's just like this weird process, where that's a terrible setup too, because what happens in the history of that is you have to choose one or another.

Okay.

If you do either poorly, you're, you're like, you're like somehow in a third worst place.

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

Cause your team T guy, you become dumb pipes.

Okay.

That is the, the Azure becomes, what is it?

Charter, right?

Yeah.

But then the other version of this is you say, no, no, screw these guys.

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

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

That really sucks because the stock is very much weighed on out your Azure revenue.

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

I think about that all the time.

What would this trade X X Azure?

This was just Microsoft.

Eight times earnings, 10 times earnings.

It was trading like that before, actually.

Oh, geez.

And remember the 2010s era, when it went all the way down to 10 times earnings in the Steve Ballmer era.

And then it inflected outward as it did with Azure.

Yeah, yeah.

Azure and 365.

Yeah, there we go.

That's right.

Yeah.

Okay, I don't think you have the answer, but I just like this is one of the most bizarre.

I want to call it f, but I don't know if it's a f 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?

So I think the biggest blunder of all time, that the part that like is kind of crazy to me about that one is like yeah I, I definitely think there was a financial decision, because when you look at it it looks like a conversation of shareholders and roic, and how much are you willing to burn cash?

Because, like you know, effectively you look at all the other peers and google, i would argue, is going to free cash with zero.

I think meta will go to free cash with zero.

Microsoft is still, like you know, satya did not make the company.

He is a professional manager and there is a board and there's a conversation.

He's being responsible, right?

But the problem is that responsible, this is like an innovator's dilemma, right?

Like, do I maintain, maximize shareholder value and cash flow today?

Or do I have a deep belief that AI will kill the hell out of my core business and I need to all in invest in 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 pill.

Okay.

He doesn't have any fear of the show.

He thinks it's just like a new... It's a new loaded... Lotus and Excel came around, right?

It's just a new tool.

But I think at the same time, 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 mode.

Right.

Because the dream of this in theory.

You're like oh remember, in twenty three when they did the first big deal.

You're like wow, Microsoft's going to win it all because they already have all the distribution and they're going to have the perfect product.

And boom, they're going to have this giant business that makes them, you know whatever 100 billion, 100 trillion dollars.

OK, whatever number you want to say.

But reality is, Claude for Excel, Claude for PowerPoint, is literally exactly what it's supposed to be.

Microsoft should have built it.

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 the tribe over the hill.

Yeah, yeah.

This is the tribe over the hill?

Yeah.

And the tribe over the hill is like on a nightly raid, easily sack the hell out of your castle.

And you're like, dang, this is an issue.

So Microsoft now is super stuck in the middle.

And so how they're going to have to do this is totally different.

I think they're going to keep pulling back in.

We're starting to see that like they're going to do internal training.

They're going to try to do more foundational models.

They're going to try to use the weights they have access to.

Is MEI?

Yeah.

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

Well, you know, it may be seen.

They do have.

You know, they are one of the big companies in the world with all these resources.

Yeah.

I just want to push back on the sort of responsibility part.

Like, you know, so Oracle picked up the slack.

Yeah.

Is Oracle being irresponsible?

You know, 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.

OK, like the thing is, like, I think the slack they should have done it.

But like the whole setup, in my opinion, on Oracle is own goal.

They messed up the messaging.

They messed up the fundraising.

And, in my opinion, if they were not like like one of the things that happened is they went so aggressive out the gate, did the quarter where they said like four hundred billion dollars.

Right.

They said are in prize the world.

They promised the world.

Then they proceeded to raise as much money as possible.

And like this is the first time they've ever done these giant build outs.

And so now there's delays.

Everyone's like, whoa, you did this much, right?

Capitalism is kind of like, hey, pump the brakes.

And seriously I think that if they just tiered it out better meaning that they didn't do it all in one period played a little bit of expectations management.

This year's revenue from the deployed GPUs should partially help start to keep self 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 just offered so much debt.

It's kind of funny because in high yield TMT, It's such a big part of the entire index.

Like the issuance is so big.

It's like debt index.

Yes.

I have zero familiarity with this stuff.

I'm pulling some numbers up.

I did the numbers forever ago.

I'm like, I hallucinate in whatever, forget all the precision.

Let's just say all of the investment grade TMT is like 500 billion.

Okay.

I think Oracle is like 135 of it.

So that's like that's so big.

And so each time you have to, you put up a huge new issuance, you have to give someone an incentive to go buy your debt instead of someone else's.

And so you just kind of like they're screwing up the liquidity because these issuance are so big, diluting the whole pie.

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

So the entire index is selling off.

Because it's like... Yeah, it's a supply.

Yeah, it's a supply thing, right?

And that's the thing that's like crazy to me is like, so they massively overshot.

And I think that we're like a weird bottleneck I never ever ever ever, ever thought would ever, ever hit.

And I think you could appreciate this uniquely is like one of the bottlenecks, is like supply of debt into the market.

Capitalism cannot 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 want it, they turn around and they're like hey, instead of like, can you give me a 10 billion loan limit?

Like we've never done that before, right?

So the absolute size is kind of screwing it up.

And I think that Oracle specifically, was way way, way too aggressive into a relatively illiquid market.

And so, like you have to do this, like you have to kind of let yourself into it if it's going to be like that.

But they like super jolty to 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 hear an all right one?

My sister-in-law could have just internally funded this.

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.

It'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.

This is correct.

And just from the math perspective, no one else, they're better than Oracle.

Just by their credit rating, they have a 2% more profitability on a capital basis.

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 kind of wavering.

To win, you have to be really bold, right?

And they're kind of doing this one thing over here, being really defensive with Copilot.

Satya is now the product manager of Copilot.

And then they're also pulling back from Azure.

Meanwhile, the competitors are pushing in for the supply.

It's a really weird game.

I think Microsoft has to choose a direction.

We'll see.

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.

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 now you can.

And Google's open as a supplier, I guess.

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 the whole DeepMind story was we will hoard all the TPUs because we were first, you know?

And so, like, why sell?

Why give anything to the public?

I think it's because at least last year, pre-Gemini 3, it was like, dude, we have all these TPUs.

We're going to hoard them all.

But people aren't using our profits anyways.

And hey, 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 break this down.

One, we wrote about it in TPV8, like whatever we think Rubin will be much more competitive.

I think Ironwood V7 is the peak gap between TCO, between NVIDIA and TPU.

Right.

So if you are at your absolute strongest point, what do you do?

There's two ways you do it.

You could try to maximize and like squeeze the juice and like make margins, or you can, you can gain market share.

I think the perspective of doing this externally with Anthropic is to gain market share.

Because the biggest gap you have and one of the reasons why there hasn't been a second merchant chip and also, you can argue, NVIDIA's most valuable company in the world.

What's the value of TPU in Google?

It's huge.

You must have done the math.

I've done the math.

It could be like, it's like a trillion.

It's like a trillion.

Yeah, it's like a trillion or something like that.

Assuming it gets like 30% market share or something like that.

Everyone has been trying to crack the merchant silicon mode, right?

And now they have the biggest absolute outperformance.

A lot of the people who did the original TPU program are like now at OpenAI.

Some of them are medics.

Yeah, exactly.

Some of them are medics.

They're all over, right?

The core team that did most engineering have since really dispersed.

And so I think the gap might close over time.

And so at this absolute period of time, they're going to win market share.

And 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.

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 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.

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 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 using.

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 InferenceX will have TPUs eventually.

That's something we want to do longer term.

I think that that will really show in numbers.

As a benchmark?

Yeah, as a benchmark.

How do you expect them to come in?

Pretty good on a price basis.

I mean, our expectation is they're the best TCO by a meaningful amount right now.

Anthropic's very clear how they feel.

Everyone is very clear I think even OpenAI would take.

I think everyone would eat as much TPU v7 as possible if you had it in a perfectly unconstrained world.

It would probably be at this exact moment, like you know, the hottest kid on the block until Reuben comes out.

But the reality is supply chain really matters.

And that's just, that's not available.

And so that TCO advantage is at this absolute biggest aperture.

Then like Jensen essentially 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 can you do?

It's this cascade, right?

Which I think you've talked about.

Yeah.

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

Yeah.

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

I think there's a link I should be pulling up.

Yeah.

That's it.

Yeah.

So, so yeah, it all goes back to the fabs.

It all goes to who's making the chips.

And like I think one of the big differences too is just like the per.

It's just like a really good, cleverly designed system architecture and it's relatively stable and it's clear that they you can pre-train big models on it, which is like a huge advantage.

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.

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 Rubin.

And that's when your special window starts to close.

What's the technical reason why?

HBM.

APM 4 versus 3.

And that's a secure strategic decision by NVIDIA?

Yeah, I think one.

So NVIDIA is always if you think about NVIDIA, they're always trying to gas it as hard as they can.

It is a high-performance chip.

It is an F1.

It is as maxed out as possible.

TPU is kind of like this replicatable pod with very high stability, right?

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

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

But I think GB200 would have completely mogged, you know, V7 if it came out on time and stable.

It came out a little delayed and it wasn't stable.

And so I think there's a lot of different ways to kind of course correct that.

And the one thing that's important is like, I think on the infrastructure side or on the supply chain side, bar none NVIDIA is the best.

They own the entire supply chain.

They really do.

Like you think, all those 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 everyone, with all the Korean guys, all the TSMC.

He's doing the shots with everyone.

Why do you think he's doing love shots with everyone, okay?

It's because he needs to get the chips, okay?

So yeah, this is Samsung's chairman.

Yeah, this is Samsung's chairman.

Yeah.

And who's the other guy over here?

But let's put it this way.

That's a huge deal.

That's a huge, huge, huge deal.

Do you think Google was out?

Hyundai, Hyundai.

Yeah.

Do you think Google was what you know?

Do you think Sergey was out in Taiwan drinking to get supply?

No, 100%.

There's an opportunity here, but there's only so many TPUs that can be made because of all the bottlenecks, right?

And so Nvidia has all the supply chain locked up.

And so they're going to have like so much of that kind of constraint there.

And so it's going to be really interesting.

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

They're going to be first on the roadmaps for even more rack density.

They're going to have like the best connectors, the best you know, the whole system will be once again 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 and everything, everything.

Yeah.

And so they're going to really jam it.

And that's that's going to be a huge advantage of performance.

One thing I love about your analysis is it's not actually just the context windows.

It's not just the KV cache.

We also have to offload it to non-HBM.

Yeah.

Every other part of the memory is it's like such an interesting cascade, a 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 read the one ratio, I'm like, yeah.

Yeah.

Okay.

So it's a three to one.

If I have three to one to four to one ratio.

I think so.

So it's in the Memory Mania post that we just put out of like the four to one or the tradeoff ratio.

Yeah.

Scroll down somewhere and you'll see.

Yeah.

So basically for listeners it's the idea that, like when you convert to HBM because there's a huge demand for HBM, it takes three times one.

HBM sort of units is like three times of the other sort of DDR or whatever.

Right.

Yeah, so some amount will always be lost in production because yield isn't perfect.

And so effectively, you're trading some like you're trading.

I actually wrote a really funny piece like.

I called it like super oil, but let's say, this is a better one.

But pretty much like 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 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 with like the biggest shortage ever in NAND and DRAM, like terrible, like terrible catastrophic, the worst one ever.

Like the last analysis I could put to is like 96 or something like that.

Seriously, it's like a history one.

And then meanwhile, we have all this new demand, HBM specifically the highest end, you need the most memory.

The trade ratio is crazy.

So each, you know, each bit of HBM is essentially a 4x multiplier onto DRAM.

And then now, so we completely constrained, took all the D room capacity.

We just came out of this shortage.

So no one invested in any clean rooms or capital equipment or anything like that.

People got like massively free cash flow negative.

No one's spending a cent.

Okay.

People could go bankrupt, you know?

Yeah.

So they haven't invested in these three year long lead time items.

And then now there's like more demand than God.

And it also evaporates the middle layer because the KB cash offload.

And then boom, you're just looking at the supply demand.

You're like, yeah, this is not going to catch up for like 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.

And so now, we're a few years later.

And all the supply is gone.

So, I mean, people are I mean, it's just it's crazy.

We our posts, our conclusion is like we could see DRAM prices like go up 100 percent again.

Like it's, it's going to be the point where and this is like also example like really interesting in the whole thing

Another 100 percent, I think, is demand destruction.

I think you will start to have demand destruction.

What does that look like?

Where hyperscalers maybe purchase less or something like that on the margin, on the margin, right?

Because they're like, okay, well, what if I just really focus on this energy aspect instead?

And also ironically, all the energy.

So like every not every data center in America, but like many many many, most of the data centers in America are delayed.

So you had this thing that's supposed to come on in 12 months, it's coming on in 18.

Maybe what you can do is you can play chicken with memory prices and you can kind of push out.

Of course, everything you have in the pipeline, you pull forward as hard as you can, okay?

You pull forward, you double, triple order, and then the DRAM and HBM guys are like oh my God, 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 memories of capacity.

Yeah well Nazis, oh yeah, you could say SK, Hynix and Sam Micron, Micron and all the all the semi, like semi-cap has been ripping off, which is like, by the way, when I was in Belyazny, a majority, a lot of money we made was just being along Micron, Yeah.

In the last like, 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, and also for listeners it's gonna affect like, your phones yeah like yeah Apple, I think Apple's.

I had to buy an SD card for this thing.

Yeah, that's.

That's nothing too, Because that's just the NAND side.

Dude, have you looked up like, I want to say like 64 gigabytes of DRAM?

Like, I'm moving up, like, I need to refresh my iPhone.

I'm moving it up because I'm doing this research.

Oh, yeah, you need to do it as soon as... Buy your iPhone now.

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.

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

There's a whole super-duper deep in the weeds.

There's this whole technology that was very focused on cloud era, called CXL, which is memory expanders for CPUs in order to have whatever, just like Elastic pulls a compute of CPU and DRAM and whatever memory attached and whatever.

It never really took off because essentially HBM was like the way that really crushed it all.

High performance, best of breed wins.

But this CXL technology that kind of never really took off, is going to take off just because what they're going to do is they're going to take DDR4.

They're going to take the oldest, every bit of spare memory they can find, and they're going to put them into racks and then they're going to attach them via CXL.

So like this...

Oh, exactly that.

Yeah, it's exactly that.

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

Like, yeah that you know, I was like a CXL bull for once upon a time and then became very clear it was going to die.

And I was like, it's back.

But only because...

The entire Express intent is to have these DD like old chips.

Pull the old chips, attach it to something new.

That's what it's going to be like.

The memory shortage is just like, it's crazy.

Yeah, it's incredible.

Yeah.

So obviously this is lower level than I usually go to, which is why I'm having so much fun.

One thing I do tell people about is well, everyone including Sam, by the way is predicting longer context windows.

We've been kind of effectively stuck at a million for two years now.

I've actually been thinking about that a lot.

This is not going to go to 100 million context windows.

It's not going to go to a trillion.

This is it.

Yeah.

This is it for like five years, 10 years.

Pretty much.

Okay, so the question is, I mean, yeah, probably, actually.

Will capitalism work?

Will there be a way for supply to show up?

Probably.

But on top of that I wonder if there's going to be like history of compute.

What happens is you have to like.

You have to like.

Make a curve of the context windows.

Like, does free context windows go to, like, 1000.

Hey, you can use chat GPT free now, but you your context window is like 1000 tokens or something like that.

And then you just like somehow do a tiny, like a tiny parcel for that, just so that you can then charge, like you know, 100x more for 1 million.

The 1 million context window is like a mansion, you know, That's the real.

You live in a mansion.

I live in a mansion right now.

Yeah.

Oh my God.

The word just context rationing just came to me.

I'm like fuck, like we're going to have like vouchers for like.

Okay, you can have this amount of context today.

It's like.

Yeah, you're going to 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, like if you're thinking about chips, but in the AI world.

I just am like always been curious because it does feel like, at least in my stated experience really long context.

Like you see in the papers they kind of like drop off, they like actually don't use all the context.

So that's like kind of the thing I've been most interested in is like does the 100 million context actually matter if, if it's not possible to use it all,

Versions of 100 million do exist today.

They just suck in various ways.

They're not actually applying full attention, right?

You can use a 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, like 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 just not, we're never going to use all of them, but we'll improve, like 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 there's a physical constraint.

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

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

Yeah, what's the point, yeah?

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 budget or something.

I feel like everyone's going to be like, whoa, whoa, whoa.

You're running out of context window today, you know?

Like, maybe that's what happens next year.

We're charged on context window.

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

Yeah.

I've been pretty interested in that.

But, like, to be clear, I'm a total idiot.

I have no idea what Claude told me.

You're the semis guy, man.

You're really good at your stuff.

One thing I wanted to spot check on was Talos.

I have not messed around with it.

Okay.

You don't have to mess around with it.

It's just this general theory of custom ASICs burning the weights into the chip.

Yep.

So you don't need memory.

Yep.

That's pretty good, actually.

Right?

I think that that makes sense to me.

This comes at the perfect time.

It does, but I guess... Okay, so historically, the question is how big does it scale, right?

But, like...

I mean, you know a lot of the models are kind of actually smaller than you think, right?

Sorry, what do you mean?

A lot of the production.

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 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.

We've been using the same groups.

Yeah, that makes no sense, just in order for the compute to be even remotely okay.

It makes sense to you.

And I mean TBD on a sort of practical implementation, but otherwise burning the waste into the chip.

Why didn't Etched or some of the other guys get there first?

Um, that's just pretty interesting.

I don't know.

I mean, I'm not kind of like super speculate.

I mean the thing, the thing is like, their thing is like okay, how do we have a big systolic array?

Right.

But like they didn't burn weights into the chip.

That's all different.

Right.

I mean like, look like.

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

Yeah.

Yeah, that's the fastest way possible.

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, like, going to be the perfect solution.

There is definitely a world and space and like a design space where they're going to be very valuable and cool, but like also, the reason why my hit rate for every AI accelerator trip is like, very Like.

I just don't believe in them is because like, where are they?

Until Cerebrus and Grock, honestly, they were all considered failures.

And even then, we're like, what are they going to do with Grock?

What are they going to do with Cerebrus?

Is Summon Nova...

No, I think someone I was like a much more interesting one, but I think there's like there's like all kinds of deal issues with that.

I haven't been keeping up with that one.

Yeah.

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

Yeah.

Cause I kind of forget about them too.

But yeah, I honestly I was going to say they were.

You know, once a year they show up.

Yeah, they, they do.

And they're not, they're not so bad.

Yeah.

You mentioned, actually, some CPU shortage stuff, or CPU .

What's going on there?

I think it's OK.

So 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.

It has been done a lot.

I like, and like me, this is pure, like, you know, a schizophrenic hat brain.

Cause I have a schizophrenic trend hat brain.

I'm wondering if it's two things shipping, vibe, code, slop to prod.

That's number one.

That's definitely something that's possible.

But it's happening to all the clouds at once, I feel like.

It's not just the AWS thing.

It's not just like a GitHub Azure thing.

We are kind of right at the exact five to six year period of the refresh cycle of COVID.

So COVID, we had this big 2020, 2021, you bought like $100 billion of CPUs and stuff like that.

And so we're right at the natural end of life for these chips.

And so usually what you do is you have this big refresh of all these chips.

But what?

What's been happening instead is everyone has essentially scrounged all of their budget as hard as they can.

But then, like, I feel like I've seen it in, like, Azure.

Like, hey, last night, my Amazon Prime thing doesn't work.

And I was like, it'll probably work in the morning, babe.

Don't worry about it.

I think Azure's just are, like, AWS is pissed tonight, you know, something like that.

But I think, so we have this five-year thing.

Everyone's scrounged every single dollar they could to essentially invest in as much as AIs as possible and just do maintenance CapEx on CPU.

And 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 actually heavily used for RL gyms.

You have to simulate software, and it uses a lot of CPUs.

So not quite the orders of magnitude of the GPU stuff, but it's just such a big trend even when it steps slightly in a place.

Massive amounts of demand.

I feel like we might actually be seeing a CPU shortage, partially because of this refresh cycle, but partially also because, like I legitimately believe, the cloud code.

Cloud code is increasing software creation.

And then on top of that, there is real demand from RL.

Yeah, yeah.

And just general production agents as well.

You know, we just, yeah, like RLMs take compute and, you know, open cloud takes more compute.

And it's just a different slope, but at the same sort of direction.

But still an upslope in a slope that, to be clear, has had massive underinvestment for the last two years, because everyone was like The same problem that happened.

Massive underinvestment 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.

Semi's guys say Semi's numbers go up.

That's one way to put it, yeah.

The thing that's crazy is we talked about the demand.

It's like, you're right.

I mean, for sure.

Like, show me where I'm wrong.

Yeah, show me where I'm... I mean, definitely not.

But the thing that's crazy is memory prices are going to go up so much that we're going to have to choose which Exactly.

That's the crazy part to me.

Historically, memory has never been a constraint like this, where it said actually 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 can't do these things because your price is out of market.

That's what's crazy.

That is the first thing that's happened in a long time.

That's going to be really interesting to see where that shortage and how it's like digested and felt.

It was amazing.

Thank you for that breakdown.

I feel like I really understood it when I was talking to you.

So we're going to transition to a couple of personal things.

Yeah, sure.

How do you write?

Because you write a fuck ton.

Yeah, I do.

I have been writing a little bit less these days now that i'm like in the semi-analysis mega mind.

I definitely write a lot and, like you, kept going with fab because for a while okay, dude to clear, that was really so so.

So look, i'm still trying to do fab because i i i do feel deeply connected to writing.

Let's just specifically talk on this a little bit.

Yeah, just just just like explain yourself you, Okay.

So the thing before LLMs came around, the thing I felt the strongest about 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 semiconductor book I literally raw dog some textbooks, whatever.

The comprehension was not very high, but like Hey, whose comprehension is you know but I was able just to like push through these books and learn.

So I've always loved reading.

That's like my, my number one original competitive skill set differentiator and also something I like loved as a kid crazy reader when I was a kid always have been and then starting the sub stack, which has been really fun actually, because I just really wanted to get my story out, like the things I cared about.

Close the loop for writing for me because I love I love reading so much.

It makes a lot of sense that I love writing.

I think what really helped is I wrote every single week for like since October 21, like consecutive streak for a long time.

The streak has been a little broken as of late.

Semi-analysis plus fabricated knowledge is pretty hard to do.

Like all of 24, I think, like we're just talking just like Every single day, every single week, I will put something out right.

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

It was a hard rule, one a week.

At least an attempt to.

And so I think one of the best ways, all the people who write about writing all say the same thing.

You need to just be writing hard.

And so that's how I start.

I'm writing every week, they know.

Yeah, it really helps.

Well, I was going to say, what's crazy is like, it's kind of hard these days.

And LLMs kind of have really, I don't know, I don't like LLM writing.

I do like it for ideation, like making outlines.

Yeah, yeah, research.

Here's my unorganized thoughts, make it into an outline.

And then, like you know, I'll even be like, put bullet points in the outline and I'll literally read the outline and then like, ideate and write in parallel.

But yeah, that's how I feel about writing, I guess.

Write more.

I have a strong, for nonfiction writing, I really like this book called On Writing Well.

That's just a really good classic book.

It's actually summarized and synthesized into a skill for me.

Oh, yeah.

Yeah.

So, hey, please edit this.

Use these.

Use this style guide.

Use the learning from this book.

So yeah, just stuff like that.

Yeah.

Okay.

And then do you like have a topic idea list that you groom?

Like I've put mine in Apple notes now, but it's bro.

It's no, never, never.

I'm one.

And I'm just, I'm just a one shot.

Whatever is on your head.

Yeah.

Usually I one shot the idea all the way.

Yeah.

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 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 cook Yeah.

And so this is async and this is sync, right?

This is like passive, like, oh, here's a data point.

Here's a quote.

Here's a thing.

I'll just slot it in the right thing.

And then I bake it.

Historically okay.

So how that pre-writing actually works today is probably in the semi-analysis slack.

It's just like all the little things.

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 prewriting, think about it, all that stuff, and go to sleep and wake up.

The next, the fresh context window in the morning is my number one advice on writing.

Helps you decode better?

It helps so much better.

Like literally, if I'm like hey, I need to write something right now, I will do, I'll write it all down, I'll make outlines, I'll do all kinds of crap except for writing it.

And then I'll be like, and I'll go to sleep and then wake up and the first thing I do I'll open up a new tab and I will write it.

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 how I take it from there.

Cool.

Amazing.

Last thing, hike.

Yeah.

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 a 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 opening.

I took a break a year off to bike through Japan.

How could you?

You're going to miss it.

You're going to miss everything.

But he's like, I'm good.

I'm having kids and whatever.

You did a sabbatical as well.

And it was pre-AI.

But it was interesting.

You did the Appalachian Trail.

So there's three big ones in the United States.

It's the Appalachian, the Pacific Crest Trail, and 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 older bad, whatever.

But like honest to the pc, they're all different trails.

Like i'm pretty steeped in hiking culture, i think mile for mile at is actually the hardest.

But i did the cdt as my first trail, as my first through hike.

You know you learned a little bit about the three when i was choosing which one i wanted to do and the cdt was the one that scared me the most.

I was like hey, this would be the hardest, biggest accomplishment i could possibly imagine.

And I thought, if I never have an opportunity to ever do this ever again which so far seems to be pretty correct which one I'm 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 chose the Continental Divide Trail.

I did that in 2021. pre-AI and... But after the GPC 3 SA.

After the GPC 3 SA, yeah.

I felt like I was missing out a lot and there's like a huge... It was a huge year for Substack.

I feel like I missed out like a very big year of like the big growth.

You're doing okay.

I'm doing fine.

But 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 and I would definitely do it again, but I probably for people it's like four months, five months, six months, six months, six months, six months, 2800 miles.

We'll we'll call it on the route 2850 or whatever the miles I went.

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

Mostly alone.

Did it alone.

You get the trail name.

It's a whole audio books.

I listened to audio books until I hated them.

Listened to music until I hated it.

Got bored as hell.

Like, you just... You go through all of it, actually.

Yeah.

Yeah, it was awesome.

Six months.

I think... The thing I think about is...

So far in most in my life.

Up until that point you 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 city about like it's just like I like I like the outdoors.

I like hiking.

I'm like good at it, whatever.

Just something I really appeal to me from an adventure perspective.

Like when in modern life you get to say, hey, I'm going on an adventure.

Never.

And that's what it was.

It was an adventure for me.

And one that I got to really... You know, it's like, oh, the journey is the destination or whatever.

You learn a lot about yourself, in fact.

I learned... It didn't grow me up, per se, but I feel like I...

And more well-defined of my view of myself.

I understand how I react.

I actually know where my exact line or it's like, you know, you're like, oh, I'll go do this.

It's like, actually, no, I know my exact line where I'm like, I would not do that.

I know exactly where I'm not.

That's too scary, too hard to whatever.

Yeah.

I know my limits a little better.

I feel like I know just more about myself.

It is a very condensed version of a very intense life.

And yeah, I wouldn't give up that experience for anything in the entire world.

It was extremely personally meaningful to me.

I think it's very fun to go back to the lower part of the Maslow's hierarchy of needs.

Like all this crap we're talking about today is so abstract.

It's like totally fake.

And we were not born and built for it.

We were born to like, you know, our human evolution got us to like scrape a living in the mud.

Okay.

Hunt and gather.

Hunt and gather and just not die.

It's kind of interesting to go backwards and to see what feels like.

Dude, I was so hungry, so scared, so alone, but also like super low.

The phrase is like lowest lows and highest highs.

These crazy lows where you're like, what am I doing?

What does all mean?

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

And so, yeah, I wouldn't, I highly recommend it as very, I would do it when you're younger.

I wish I did it after college.

Yeah.

Like right after college and said, Hey, like whatever kicked us out a year.

I think it's good to learn about yourself.

It's really important.

Your self-mastery is your most important tool use of all.

So yeah.

Self-mastery is the most important tool use.

Yeah.

Amazing.

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

Yeah.

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

Yeah.

Thank you.

Thank you for having me.

Yeah.

Great to catch up.

[The AI-Driven Future of Information Work: Insights from the Semiconductor Frontier]-[Claude Code for Finance + The Global Memory Shortage: Doug O'Laughlin, SemiAnalysis]

📋 Summary

The 'Junior Analyst' Paradigm Shift

The 'Cloud Code' Awakening

Memory Constraints and the 'Context Rationing' Era

The 'Railroad' Analogy for AI Capital

The Future of Information Work

🎯Key Sentences

📝Key Phrases

📖 Transcript

[The AI-Driven Future of Information Work: Insights from the Semiconductor Frontier]-[Claude Code for Finance + The Global Memory Shortage: Doug O'Laughlin, SemiAnalysis]

📋 Summary

The 'Junior Analyst' Paradigm Shift

The 'Cloud Code' Awakening

Memory Constraints and the 'Context Rationing' Era

The 'Railroad' Analogy for AI Capital

The Future of Information Work

Deep Learning with ListenLeap

🎯Key Sentences

📝Key Phrases

📖 Transcript

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